HyperPy: An automatic hyperparameter optimization framework
Project description
hyperpy
HyperPy: An automatic hyperparameter optimization framework
Description
HyperPy: Library for automatic hyperparameter optimization. Build on top of Optuna to perform hyperparameter optimization with low code.
This library corresponds to part of the work of Sergio A. Mora Pardo
Installation
You can install hyperpy
with pip:
# pip install py-hyperpy
Example
Import the library:
import hyperpy as hy
from hyperpy import ExampleConfig # Just for example
Reading data:
data=ExampleConfig()
train, test, sub = data.readData()
Extract features:
feat_X = train.filter(['Pclass','Age', 'SibSp', 'Parch','Fare']).values
Y = train.Survived.values
Run the optimization:
running=hy.run(feat_X, Y)
study = running.buildStudy()
See the results:
print("best params: ", study.best_params)
print("best test accuracy: ", study.best_value)
best_params, best_value = hy.results.results(study)
NOTE
The function hy.run()
return a Study
object. And only needs: Features, target. In the example: best test accuracy = 0.7407407164573669
Documentation
Documentation is available at hyperpy
Working on tutorial, meanwhile explore documentation.
Development
Source code is available at hyperpy
Contact
Project details
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